BBA 4TH SEMESTER
BBA N 406: OPERATIONS RESEARCH
Operational Research Definition:
“Operational research is an analytical method
of problem solving. and decision making that is useful in the management of
Organizations. In operational research, problems are broken down into basic
components and then solved in define steps by mathematical analysis.”
“Operational research is a scientific approach
to program solving for executive management”
Characteristics (Features) Of Operations
Research
System Approach: OR
studies the situations or problems as a whole. This means that while
arriving at a decision, an OR team
examine the relative important of all conflicting and multiple objectives and
the validity of claims of various departments of the organization from the
perspective of whole organization.
Inter- Disciplinary Team Approach: No single individual can
have a thorough knowledge of all aspects of the undertaking. So the Team
Approach give the better results.
Optimization: The purpose of operational research is to achieve
the best performance under the given circumstances it also involves comparing
and narrowing down potential options.
Simulation: This invoice building models or replications
in order to try out and test solutions before applying them.
Methodological Approach: OR utilizes
scientific method to solve the problems. The scientific method consists of
observing and defining the problems; formulating and testing the hypothesis;
and analysing the results of the test.
Decision Making: OR is a problem solving and a decision making
science.and it is a systematic process.
Probability & statistics: This
includes using mathematical algorithm and data to uncover helpful insights and
risks, make reliable predictions and test possible solutions.
Scope of Operational Research (Applications)
An Operations uses some valuable resources like
men, machines, money, materials, time,
efforts , etc.
The main applications of OR as following-
Financial
management:
1.
To minimize the capital required to maintain
any level of business.
2.
Finding out long term capital requirements.
3.
Credit policies and credit risks
4.
Break- even Analysis
5.
Capital budgeting and finical planning.
6.
Clam and Complaint Procedures.
Production
Management:
1.
Location and size of warehouse etc
2.
Distribution policy
3.
Allocation of resources
4.
Designing and selecting sites
5.
Calculating the optimum product mix.
6.
Maintenance policies and preventive
maintenance.
In
agriculture:
1.
Determination of climatic condition
2.
Optimal production
3.
Optimal distribution of water from the
resources
Research
and development:
1.
Determination of area of concentration for
research and development
2.
Control of development projects
3.
Co-ordination
among multiple research projects
4.
Determination of time and cost requirements.
Human Resource Management:
1.
Retirement age
2.
Job assignment
3.
Promotion policies
4.
Putting right man on right job
5.
Recruitment policy
6.
Wages/salary
administration
Market
Management:
1.
Product selection and competitive action
2.
Advertising strategy and media selection
3.
Best time to launch new product
4.
Knowing customer’s requirement.
Advantages of using approach in business and
decision making
1.
Better control
2.
Better decision
3.
Better coordination
4.
Better system
5.
Increase profit
Scientific Methods in OR
Generally there are three phases:
1. Judgment
Phases: this phase includes:
A.
identification of the problem
B.
Establishment of an appropriate objective.
C.
Determination of various measures of
effectiveness
D.
Formulation of an appropriate model of the
problem, abstracting the essential information, so that a solution to the
decision maker’s goal can be obtained.
2. Research Phase: This
Phase is the longest among other two Phases
A.
Observation and data collection for a better
understanding of the problem.
B.
Formulation of hypothesis and model.
C.
Observation and experimentation to test the
hypothesis.
D.
Analysis of the available information and verification
of the hypothesis
E.
Predictions of various results
F.
Generalization of the various results and consideration
of alternative methods
3. Action Phase: This phase consists of making recommendation
for the decision process by those who passed the problem for considerations or
by anyone one who is in the position to implement results.
Methodology
of OR
1.
Formulation and definition of the problem.
2.
Construction of a mathematical model to
represent the system under study.
3.
Collecting data required by the model
4.
Deriving a solution from the model.
5.
Testing the model and the solution derived from
it.
6.
Establishing control over the solution.
7.
Validation of the model
8.
Implementing and maintaining the solutions
Operational
research models
Operational research
models are classified on the basis of features of a typical problem under
investigations are considered, for example photographs, roadmaps,
organizational Charts, etc. The objective of the model is to provide a mean for
analyzing the behaviour of the system for the purpose of improving the
performance.
1. classification by structure
a. Iconic (physical) model:
-
Iconic models are pictorial representation of
real system and have the appearance of the real thing. for example, A Photographer;
Blueprint a globe; An iconic model of earth, etc
-
Iconic model is used for teaching purpose
-
Iconic models are easy to observe, build and
describe but difficult to manipulate and not very useful for the purpose of
forecasting
-
Iconic model represent a static event.
b. Analogue (Schematic) Model:
-
These model represents a system by a set of
properties different from those of the original system and does not resemble
its physically.
-
This model represents the relationship existing
between the various members of the organization, a map show Road , Highway
towns and their inter-relationship etc.
-
Analogue models can be represents the dynamic
situation
c. Symbolic Mathematical Model:
-
There
are most abstract in nature the employee a set of mathematical symbol (letters,
numbers,etc) to represent the components (and their relationships) of the real
system
-
These models are most general and precise and
can be analysed and manipulated by using laws of mathematics
-
Example- Relationship among velocity, Distance
and Acceleration etc.
2. Classification based on functions or purpose
a. Descriptive Model: These
model simply describe some aspects of a situation, based on observation, survey,
questionnaire results, or other available data of a situation and do not
predict or recommend anything. for example, organizational chart, block diagram
representing an algorithm, or plant layout diagram.
b. Predictive Model: These
models are used to predict the outcomes due to a given set of alternatives for
the problem
-
These models do not have an objective function
as a part of the model of evaluating decision alternative based on outcomes or
pay off values
-
Example: S = x + yE +zI is a model that describe how the sale (S) of a
product changes with a change in the advertising expenditure (E) and the
personal income (I) Here x,y,z are parameters whose values must be estimated.
c. Normative ( or Optimization )or (Prescriptive )
Model: These models provide the optimal solution to problem subject
to certain limitations on the use of resources for example: In linear programming models are formulated for
optimizing the given objective function.
3. Then classification based on Time Reference
a. Static Models:
- This model represents a system at a particular point of time and do
not take into account change over time
-
It represents a system at some specified time.
-
Example,
An inventory model can be developed and solved to determine an economic order
quantity for the next period assuming
that the demand in planning period would remaining the same as that for today.
-
b. Dynamic model: These model consider
time as one of the important variables and admit the impact of change is
generated by time, Thus a sequence of inter-related decision over a period of
time are made to select the optimal course of action to optimize the given
objectives
for example Dynamic programming
4. Classification based on Degree of Certainty (by
Nature of the Environment)
a. Deterministic Model: All the parameters and functional relationships
are assumed to be known with certainty when the decision is to be made.
- Example- linear programming, Break Even Analysis.
b. Probabilistic (stochastic) Model: If at least one
parameter of decision variable is random variables, then the model is
probabilistic model.
-
These models are most general and precise and
can be analysed and manipulated by using laws of mathematics
-
Example- Relationship
among velocity, Distance and Acceleration, Cost-Volume-profit model
5. Classification based on Method of solution of Quantification
A. Heuristic model: If
certain sets of rules are applied in a consistent manner to facilitate solution
to a problem.
-
These models
requires and ample amount of creativity and past experience but they operate
faster as compared to other models
-
Very useful to solve large size problem
B. Analytical model: These
models have a specific mathematical structure and thus can be solved by know
analytical or mathematical technique
-
For example in a programming model,
transportation and assignment model
C. Simulation model: These
models also having a mathematical structure but are not solved by applying
mathematical techniques to get a solution
-
Simulation models are more flexible than
mathematical model and can therefore, be used to represent a complex system
that cannot be represented mathematically
-
These models to do not provide the general solutions
like those of mathematical model
-
Advantage and limitations of OR Models
Advantages:
·
It
provide some logical, scientific and systematic approach to the problem
·
It helps incorporating useful tools that
eliminate duplication of methods applied to solve any specific problem
·
Model construction provides the economic
description and explanations
·
It helps
to finding a venue for New Research and improvement in the system.
·
Limitations
·
Models
are only an attempt in understanding operations and should never be considered
as absolute in any sense
·
Validity of any model with regard to corresponding
operating operations can only be verified
·
Model construction requires the services of
subject experts
Basic OR Techniques
1.
Linear Programming
2.
Queuing Theory
3.
Inventory Model
4.
Network Model
5.
Replacement Model
6.
Sequencing Model
7.
Competitive Model
8.
Simulation Model
9.
Assignment Problem etc.
Limitations of OR
·
Magnitude of computation
·
Absence of Quantification
·
Gap between Managers and Operations Researcher
·
Conventional thinking
·
Money and time cost
·
Implementation
